Abstract
The objective of this contribution is to predict the development of the Czech Republic’s (CR) exports to the PRC (People’s Republic of China) using ANN (artificial neural networks). To meet the objective, two research questions are formulated. The questions focus on whether growth in the CR’s exports to the PRC can be expected and whether MLP (Multi-Layer Perceptron) networks are applicable for predicting the future development of the CR’s exports to the PRC. On the basis of previously obtained historical data, ANN with the best explanatory power are generated. For the purpose specified, three experiments are carried out, the results of which are described in detail. For the first, second and third experiments, ANN for predicting the development of exports are generated on the basis of a time series with a 1-month, 5-month and 10-month time delay, respectively. The generated ANN are the MLP and regression time series neural networks. The MLP turn out to be the most efficient in predicting the future development of the CR’s exports to the PRC. They are also able to predict possible extremes. It is also determined that the USA–China trade war has significantly affected the CR’s exports to the PRC.
Highlights
Management 14: 76. https://doi.org/Machine learning is a part of artificial intelligence, which can be characterized as a process of using mathematical data models through which a computer is learning without receiving direct instructions
Machine learning uses algorithms for identifying patterns in data, where the patterns are used for creating a data model able to formulate various types of predictions
The results showed that high prediction accuracy does not correspond to better stability prediction, but CEEMDAN-RBFNN and CEEMDAN-GWO-KNEA can still guarantee high stability in most datasets
Summary
Machine learning is a part of artificial intelligence, which can be characterized as a process of using mathematical data models through which a computer is learning without receiving direct instructions. Machine learning uses algorithms for identifying patterns in data, where the patterns are used for creating a data model able to formulate various types of predictions. The more data and experience available, the more accurate the results of machine learning prediction are (Culkin and Das 2017). Machine learning can currently be applied in many other aspects of today’s society, starting from autonomous vehicles to image recognition, health informatics or bioinformatics. According to Mosavi et al (2020), due to its adaptability, machine learning is a great option for the situations when data are constantly changing, when the character of requirements or tasks is changing, or when it is not effective to program a solution.
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